Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement
Diabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Negeri Malang
2019-12-01
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| Series: | Knowledge Engineering and Data Science |
| Online Access: | http://journal2.um.ac.id/index.php/keds/article/view/7803 |
| _version_ | 1818316215927439360 |
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| author | Utomo Pujianto Asa Luki Setiawan Harits Ar Rosyid Ali M. Mohammad Salah |
| author_facet | Utomo Pujianto Asa Luki Setiawan Harits Ar Rosyid Ali M. Mohammad Salah |
| author_sort | Utomo Pujianto |
| collection | DOAJ |
| description | Diabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the condition of diabetic patients. Knowledge of the patient's condition can help medical staff to predict the possibility of patient readmissions, namely the occurrence of a patient requiring hospitalization services back at the hospital. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care. This study compares the performance of the Naïve Bayes method and C4.5 Decision Tree in predicting readmissions of diabetic patients, especially patients who have undergone HbA1c examination. As part of this study we also compare the performance of the classification model from a number of scenarios involving a combination of preprocessing methods, namely Synthetic Minority Over-Sampling Technique (SMOTE) and Wrapper feature selection method, with both classification techniques. The scenario of C4.5 method combined with SMOTE and feature selection method produces the best performance in classifying readmissions of diabetic patients with an accuracy value of 82.74 %, precision value of 87.1 %, and recall value of 82.7 %. |
| first_indexed | 2024-12-13T09:17:54Z |
| format | Article |
| id | doaj.art-3541b8db9761433a90da86c2ee772330 |
| institution | Directory Open Access Journal |
| issn | 2597-4602 2597-4637 |
| language | English |
| last_indexed | 2024-12-13T09:17:54Z |
| publishDate | 2019-12-01 |
| publisher | Universitas Negeri Malang |
| record_format | Article |
| series | Knowledge Engineering and Data Science |
| spelling | doaj.art-3541b8db9761433a90da86c2ee7723302022-12-21T23:52:48ZengUniversitas Negeri MalangKnowledge Engineering and Data Science2597-46022597-46372019-12-0122587110.17977/um018v2i22019p58-714528Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c MeasurementUtomo PujiantoAsa Luki SetiawanHarits Ar RosyidAli M. Mohammad SalahDiabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the condition of diabetic patients. Knowledge of the patient's condition can help medical staff to predict the possibility of patient readmissions, namely the occurrence of a patient requiring hospitalization services back at the hospital. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care. This study compares the performance of the Naïve Bayes method and C4.5 Decision Tree in predicting readmissions of diabetic patients, especially patients who have undergone HbA1c examination. As part of this study we also compare the performance of the classification model from a number of scenarios involving a combination of preprocessing methods, namely Synthetic Minority Over-Sampling Technique (SMOTE) and Wrapper feature selection method, with both classification techniques. The scenario of C4.5 method combined with SMOTE and feature selection method produces the best performance in classifying readmissions of diabetic patients with an accuracy value of 82.74 %, precision value of 87.1 %, and recall value of 82.7 %.http://journal2.um.ac.id/index.php/keds/article/view/7803 |
| spellingShingle | Utomo Pujianto Asa Luki Setiawan Harits Ar Rosyid Ali M. Mohammad Salah Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement Knowledge Engineering and Data Science |
| title | Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement |
| title_full | Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement |
| title_fullStr | Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement |
| title_full_unstemmed | Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement |
| title_short | Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement |
| title_sort | comparison of naive bayes algorithm and decision tree c4 5 for hospital readmission diabetes patients using hba1c measurement |
| url | http://journal2.um.ac.id/index.php/keds/article/view/7803 |
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